Quiz 3

library(tidyverse)
library(openintro)
library(readxl)
library(plotly)
library("rnaturalearth")
library("rnaturalearthdata")
WHO_meta <- read_csv("WHO_metadata.csv", col_names = FALSE)
WHO_life_expectancy <- read_excel("WHOLifeExpectancy.xlsx", skip = 2)

Exercise 1

names(WHO_life_expectancy) <- c('country',
  'HALEbirth_Both sexes_2016', 'HALEbirth_Both sexes_2015', 'HALEbirth_Both sexes_2010', 'HALEbirth_Both sexes_2005', 'HALEbirth_Both sexes_2000', 'HALEbirth_Male_2016','HALEbirth_Male_2015', 'HALEbirth_Male_2010', 'HALEbirth_Male_2005', 'HALEbirth_Male_2000',
  'HALEbirth_Female_2016','HALEbirth_Female_2015', 'HALEbirth_Female_2010', 'HALEbirth_Female_2005', 'HALEbirth_Female_2000',
  'HALE60_Both sexes_2016', 'HALE60_Both sexes_2015', 'HALE60_Both sexes_2010', 'HALE60_Both sexes_2005', 'HALE60_Both sexes_2000',
  'HALE60_Male_2016', 'HALE60_Male_2015', 'HALE60_Male_2010', 'HALE60_Male_2005'
  , 'HALE60_Male_2000',
  'HALE60_Female_2016', 'HALE60_Female_2015', 'HALE60_Female_2010', 'HALE60_Female_2005', 'HALE60_Female_2000')

Exercise 2

WHO_life_expectancy <- WHO_life_expectancy %>%
  pivot_longer(cols = c('HALEbirth_Both sexes_2016', 'HALEbirth_Both sexes_2015', 'HALEbirth_Both sexes_2010', 'HALEbirth_Both sexes_2005', 'HALEbirth_Both sexes_2000', 'HALEbirth_Male_2016','HALEbirth_Male_2015', 'HALEbirth_Male_2010', 'HALEbirth_Male_2005', 'HALEbirth_Male_2000',
  'HALEbirth_Female_2016','HALEbirth_Female_2015', 'HALEbirth_Female_2010', 'HALEbirth_Female_2005', 'HALEbirth_Female_2000',
  'HALE60_Both sexes_2016', 'HALE60_Both sexes_2015', 'HALE60_Both sexes_2010', 'HALE60_Both sexes_2005', 'HALE60_Both sexes_2000',
  'HALE60_Male_2016', 'HALE60_Male_2015', 'HALE60_Male_2010', 'HALE60_Male_2005'
  , 'HALE60_Male_2000',
  'HALE60_Female_2016', 'HALE60_Female_2015', 'HALE60_Female_2010', 'HALE60_Female_2005', 'HALE60_Female_2000'), names_to = "type", values_to = "value") %>%
  separate(type, c("name", "sex", "year"), sep = "_") %>%
  pivot_wider(names_from = "name", values_from = "value") %>%
  mutate(HALEbirth = as.numeric(HALEbirth),
         HALE60 = as.numeric(HALE60)) %>%
  arrange(desc(year), country)

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

## Warning in mask$eval_all_mutate(quo): NAs introduced by coercion

WHO_life_expectancy

## # A tibble: 2,745 x 5
##    country     sex        year  HALEbirth HALE60
##    <chr>       <chr>      <chr>     <dbl>  <dbl>
##  1 Afghanistan Both sexes 2016       53     11.3
##  2 Afghanistan Male       2016       52.1   10.9
##  3 Afghanistan Female     2016       54.1   11.7
##  4 Albania     Both sexes 2016       68.1   16.3
##  5 Albania     Male       2016       66.7   15.3
##  6 Albania     Female     2016       69.6   17.4
##  7 Algeria     Both sexes 2016       65.5   15.8
##  8 Algeria     Male       2016       65.4   15.7
##  9 Algeria     Female     2016       65.6   15.8
## 10 Angola      Both sexes 2016       55.8   13.6
## # ... with 2,735 more rows

Exercise 3

world_map <- ne_countries(scale = "medium", returnclass = "sf")

WHO_life_expectancy_2016 <- WHO_life_expectancy %>% 
  filter(year == "2016",
         sex == "Both sexes") 

combine <- left_join(WHO_meta , WHO_life_expectancy_2016, by = c("X3" = "country"))

combine_world <- left_join(world_map, combine, by = c("iso_a3" = "X2"))

Exercise 4

my_map_theme <- function(){
  theme(panel.background=element_blank(),
        axis.text=element_blank(),
        axis.ticks=element_blank(),
        axis.title=element_blank())
}
p <- combine_world %>%
  mutate(text = paste("<b>",admin,"</b>\nHealthy Life Expectancy:", HALEbirth)) %>%
  ggplot()+
  geom_sf(aes(fill=HALEbirth+runif(nrow(combine_world), min=0, max=0.0005), text=text), color="black") +
  scale_fill_continuous("Healthy Life\nExpectancy\nat Birth (years)", low= "red", high="blueviolet",breaks=c(45,55,65,75)) +
  labs(title = "Healthy Life Expectancy\nat Birth 2016") +
  my_map_theme()

## Warning: Ignoring unknown aesthetics: text

p

### Exercise 5

ggplotly(p, tooltip = "text") %>%
  style(hoveron = "fills")